Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	127,078
2	South Dakota	121,117
3	Utah	104,931
4	Rhode Island	103,586
5	Tennessee	101,678
6	Wisconsin	99,851
7	Arizona	99,272
8	Iowa	99,249
9	Nebraska	96,880
10	Oklahoma	94,286
11	Arkansas	94,130
12	Kansas	92,544
13	Indiana	91,221
14	Alabama	89,976
15	Idaho	89,297
16	Mississippi	88,778
17	Nevada	87,991
18	Wyoming	87,943
19	Illinois	87,138
20	Montana	85,763
21	Louisiana	82,572
22	South Carolina	81,248
23	New Mexico	80,695
24	Minnesota	80,689
25	California	80,623
26	Georgia	79,378
27	Missouri	78,529
28	Kentucky	78,160
29	Texas	77,595
30	Florida	76,797
31	Delaware	76,584
32	New Jersey	74,622
33	Ohio	73,942
34	Massachusetts	72,547
35	Alaska	72,424
36	North Carolina	68,704
37	New York	68,673
38	Colorado	67,368
39	Connecticut	66,702
40	West Virginia	64,340
41	Pennsylvania	63,189
42	Michigan	59,613
43	Maryland	56,514
44	Virginia	55,350
45	District of Columbia	50,019
46	New Hampshire	45,845
47	Washington	39,990
48	Puerto Rico	39,027
49	Oregon	32,758
50	Maine	27,226
51	Hawaii	17,722
52	Vermont	17,681

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Carolina	895
2	Arizona	885
3	Oklahoma	846
4	New York	719
5	Delaware	702
6	Georgia	699
7	North Carolina	675
8	Massachusetts	670
9	New Jersey	623
10	Utah	617
11	California	589
12	Alabama	586
13	Kentucky	585
14	Mississippi	571
15	Florida	551
16	Arkansas	535
17	Virginia	501
18	New Hampshire	500
19	West Virginia	500
20	Nevada	473
21	Texas	459
22	Tennessee	456
23	Indiana	451
24	Ohio	416
25	Illinois	399
26	Louisiana	397
27	Maryland	382
28	New Mexico	378
29	Pennsylvania	372
30	Kansas	348
31	District of Columbia	325
32	Wisconsin	325
33	Alaska	318
34	Missouri	310
35	Nebraska	301
36	Rhode Island	298
37	Colorado	291
38	Montana	286
39	Iowa	285
40	South Dakota	281
41	Wyoming	272
42	Minnesota	249
43	Vermont	241
44	Puerto Rico	240
45	Maine	238
46	Idaho	220
47	North Dakota	196
48	Connecticut	188
49	Washington	179
50	Oregon	173
51	Michigan	138
52	Hawaii	101

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,358
2	New York	2,153
3	Massachusetts	2,050
4	Rhode Island	1,966
5	Mississippi	1,939
6	South Dakota	1,927
7	Connecticut	1,912
8	North Dakota	1,884
9	Louisiana	1,842
10	Arizona	1,681
11	Illinois	1,631
12	Pennsylvania	1,609
13	Arkansas	1,526
14	Michigan	1,518
15	New Mexico	1,500
16	Indiana	1,443
17	Iowa	1,422
18	Alabama	1,358
19	Nevada	1,307
20	Tennessee	1,285
21	South Carolina	1,271
22	District of Columbia	1,235
23	Kansas	1,235
24	Georgia	1,209
25	Texas	1,208
26	Florida	1,177
27	Maryland	1,135
28	Missouri	1,133
29	Minnesota	1,092
30	Montana	1,077
31	Delaware	1,069
32	Wisconsin	1,063
33	West Virginia	1,057
34	Nebraska	1,010
35	Wyoming	986
36	Colorado	970
37	California	939
38	Idaho	935
39	Ohio	916
40	North Carolina	833
41	Oklahoma	828
42	Kentucky	825
43	New Hampshire	725
44	Virginia	712
45	Puerto Rico	555
46	Washington	549
47	Utah	497
48	Oregon	446
49	Maine	404
50	Alaska	341
51	Vermont	272
52	Hawaii	239

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alabama	19
2	Arizona	19
3	New Mexico	16
4	Montana	15
5	Arkansas	12
6	Nevada	12
7	Pennsylvania	12
8	South Dakota	12
9	Wyoming	12
10	California	11
11	Georgia	11
12	Mississippi	11
13	Oklahoma	11
14	South Carolina	11
15	Texas	11
16	Massachusetts	10
17	North Carolina	10
18	New York	9
19	Florida	8
20	Louisiana	8
21	Michigan	8
22	Tennessee	8
23	West Virginia	8
24	Kentucky	7
25	Maryland	7
26	New Jersey	7
27	Illinois	6
28	Indiana	6
29	New Hampshire	6
30	North Dakota	6
31	Ohio	5
32	Virginia	5
33	Wisconsin	5
34	Connecticut	4
35	Delaware	4
36	Iowa	4
37	Minnesota	4
38	Puerto Rico	4
39	Utah	4
40	Colorado	3
41	District of Columbia	3
42	Hawaii	3
43	Alaska	2
44	Idaho	2
45	Kansas	2
46	Missouri	2
47	Nebraska	2
48	Oregon	2
49	Rhode Island	2
50	Washington	2
51	Maine	1
52	Vermont	1

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	291,041	1	99
Dewey	South Dakota	234,895	2	99
Bent	Colorado	230,052	3	99
Lincoln	Arkansas	230,037	4	99
Chattahoochee	Georgia	227,652	5	99
Davidson	Tennessee	117,499	266	91
Richland	South Carolina	81,694	1415	54
York	South Carolina	74,643	1777	43
Orange	California	74,413	1786	43
Pierce	Washington	37,273	2888	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,452	4	99
Grant	Nebraska	6,421	5	99
Davidson	Tennessee	998	1897	39
Richland	South Carolina	948	1973	37
Orange	California	831	2147	31
York	South Carolina	754	2261	28
Pierce	Washington	488	2642	15

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.